Toronto Dwellings Analysis

In this assignment, you will perform fundamental analysis for the Toronto dwellings market to allow potential real estate investors to choose rental investment properties.

Load Data


Dwelling Types Per Year

In this section, you will calculate the number of dwelling types per year. Visualize the results using bar charts and the Pandas plot function.

Hint: Use the Pandas groupby function.

Optional challenge: Plot each bar chart in a different color.


Average Monthly Shelter Costs in Toronto Per Year

In this section, you will calculate the average monthly shelter costs for owned and rented dwellings and the average house value for each year. Plot the results as a line chart.

Optional challenge: Plot each line chart in a different color.

Average House Value per Year

In this section, you want to determine the average house value per year. An investor may want to understand better the sales price of the rental property over time. For example, a customer will want to know if they should expect an increase or decrease in the property value over time so they can determine how long to hold the rental property. You will visualize the average_house_value per year as a bar chart.


Average House Value by Neighbourhood

In this section, you will use hvplot to create an interactive visualization of the average house value with a dropdown selector for the neighbourhood.

Hint: It will be easier to create a new DataFrame from grouping the data and calculating the mean house values for each year and neighbourhood.

Number of Dwelling Types per Year

In this section, you will use hvplot to create an interactive visualization of the average number of dwelling types per year with a dropdown selector for the neighbourhood.


The Top 10 Most Expensive Neighbourhoods

In this section, you will need to calculate the house value for each neighbourhood and then sort the values to obtain the top 10 most expensive neighbourhoods on average. Plot the results as a bar chart.


Neighbourhood Map

In this section, you will read in neighbourhoods location data and build an interactive map with the average house value per neighbourhood. Use a scatter_mapbox from Plotly express to create the visualization. Remember, you will need your Mapbox API key for this.

Load Location Data

Data Preparation

You will need to join the location data with the mean values per neighbourhood.

  1. Calculate the mean values for each neighbourhood.

  2. Join the average values with the neighbourhood locations.

Mapbox Visualization

Plot the average values per neighbourhood using a Plotly express scatter_mapbox visualization.


Cost Analysis - Optional Challenge

In this section, you will use Plotly express to a couple of plots that investors can interactively filter and explore various factors related to the house value of the Toronto's neighbourhoods.

Create a bar chart row facet to plot the average house values for all Toronto's neighbourhoods per year

Create a sunburst chart to conduct a costs analysis of most expensive neighbourhoods in Toronto per year